The prospective study was conducted in CH & ICH Multan from January 2021 to January 2022 to assess the significance of the proposed deep learning model that automatically uses X-rays to detect COVID-19. The development of the model proposed in the current study is based on the Darknet-19 model. When two classes are used, the proposed models detect COVID-19 infection. If three classes are used, the model classifies x-ray images as No finding, pneumonia, or COVID-19. First, the proposed model was used to classify X-ray images into Pneumonia, COVID-19, and No finding. Second, the model has been trained to detect two classes: No finding and COVID-19 categories. Our model achieved 87.06% and 97.88% accuracy for multiclass and binary tasks, respectively. Thus, it can be concluded that DarkCovidNet Deep Learning Model can be used for automated COVID-19 detection through X-ray images.

Lung cancer is one of the deadliest cancers around the world, with high mortality rate in comparison to other cancers. A lung cancer patient’s survival probability in late stages is very low. However, if it can be detected early, the patient survival rate can be improved. Diagnosing lung cancer early is a complicated task due to having the visual similarity of lungs nodules with trachea, vessels, and other surrounding tissues that leads toward misclassification of lung nodules. Therefore, correct identification and classification of nodules is required. Previous studies have used noisy features, which makes results comprising. A predictive model has been proposed to accurately detect and classify the lung nodules to address this problem. In the proposed framework, at first, the semantic segmentation was performed to identify the nodules in images in the Lungs image database consortium (LIDC) dataset. Optimal features for classification include histogram oriented gradients (HOGs), local binary patterns (LBPs), and geometric features are extracted after segmentation of nodules. The results shown that support vector machines performed better in identifying the nodules than other classifiers, achieving the highest accuracy of 97.8% with sensitivity of 100%, specificity of 93%, and false positive rate of 6.7%.